
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df_brut=pd.DataFrame()
df_brut = pd.read_csv("fr.openfoodfacts.org.products.csv", sep="\t", low_memory=False)


df_travail = df_brut.copy()


df_travail.shape

(320772, 162)


df_travail.dtypes.value_counts()

float64    106
object      56
dtype: int64


df_travail.dtypes.value_counts().plot.pie()

<AxesSubplot:ylabel='None'>


plt.figure(figsize=(20,10))
sns.heatmap(df_travail.isna(), cbar= False)

<AxesSubplot:>


df_travail['countries_fr'].value_counts()

États-Unis                                          172998
France                                               94392
Suisse                                               14953
Allemagne                                             7870
Espagne                                               5009
                                                     ...  
République tchèque,Slovaquie,Suisse                      1
Autriche,Espagne                                         1
Belgique,France,Italie,Maroc,Suisse,Royaume-Uni          1
Croatie,France                                           1
Belgique,France,Allemagne,Arabie saoudite,Suisse         1
Name: countries_fr, Length: 722, dtype: int64


df_travail['countries'].value_counts()

US                                            169928
France                                         77292
en:FR                                          16979
Suisse                                         12314
Deutschland                                     6161
                                               ...  
Tschechien,Dänemark,Frankreich,Deutschland         1
Australia,Indonesia,United States                  1
en:IT, en:switzerland                              1
France, Guyane                                     1
Deutschland,Griechenland                           1
Name: countries, Length: 1434, dtype: int64


df_travail = df_travail[df_travail.countries_fr == 'France']


df_travail.shape

(94392, 162)


df_travail['labels_fr'].value_counts()

Bio,Bio européen,AB Agriculture Biologique                                                                                  5253
Point Vert                                                                                                                  2073
Bio,Bio européen,FR-BIO-01,AB Agriculture Biologique                                                                         822
Point Vert,Eco-emballages                                                                                                    797
Labels de distributeurs,Sélection Intermarché                                                                                448
                                                                                                                            ... 
Sans colorants,Sans conservateurs,Sans additifs,Produit en Bretagne                                                            1
Huile de palme durable,Cereales-stockees-sans-pesticide-de-synthese                                                            1
Sans conservateurs,IGP,Certifie-par-qualisud                                                                                   1
Peu ou pas de sucre,Sans colorants,Sans conservateurs,Sans sucre ajouté,Clean-label                                            1
Concours général agricole,AOC,Médaille d'or du Concours général agricole,Médaille d'or du concours général agricole 2012       1
Name: labels_fr, Length: 9178, dtype: int64


df_travail2 = df_travail[[ 'product_name', 'categories', 'nutrition_grade_fr', 'energy_100g', 'proteins_100g', 'sugars_100g', 'fat_100g', 'saturated-fat_100g', 'fiber_100g', 'sodium_100g', 'fruits-vegetables-nuts_100g', 'nutrition-score-fr_100g']]


df_travail2.shape

(94392, 12)


df_travail2.head(25)


plt.figure(figsize=(20,10))
sns.heatmap(df_travail2.isna(), cbar= False)

<AxesSubplot:>


df_travail2 = df_travail2.drop(['fruits-vegetables-nuts_100g'], axis=1)
df_travail2.shape

(94392, 11)


df_travail2 = df_travail2.dropna(how='all', axis = 0)
df_travail2.shape

(88062, 11)


plt.figure(figsize=(20,10))
sns.heatmap(df_travail2.isna(), cbar= False)

<AxesSubplot:>


df_travail2.shape

(88062, 11)


df_travail2['nutrition_grade_fr'].value_counts()

d    16172
c    12631
e    11064
a     9803
b     8831
Name: nutrition_grade_fr, dtype: int64


df_travail2['nutrition_grade_fr'].value_counts().sum()

58501


df_travail3 = df_travail2.copy()


df_travail3 = df_travail3.dropna(subset=['nutrition_grade_fr'])


df_travail3.shape

(58501, 11)


df_travail3 = df_travail3.drop_duplicates()


df_travail3.shape

(58185, 11)


plt.figure(figsize=(20,10))
sns.heatmap(df_travail3.isna(), cbar= False)

<AxesSubplot:>


df_travail3.describe()


df_travail3.shape

(58185, 11)


df_travail4 = df_travail3.copy()
df_travail4.shape

(58185, 11)


from pandas.api.types import is_numeric_dtype

def remove_outlier(df):
    low = .05
    high = .95
    quant_df = df.quantile([low, high])
    for name in list(df.columns):
        if is_numeric_dtype(df[name]):
            df = df[(df[name] > quant_df.loc[low, name]) & (df[name] < quant_df.loc[high, name])]
    return df


df_travail4 = remove_outlier(df_travail3)


df_travail4.describe()


df_travail4.shape

(13160, 11)


df_travail4 = df_travail4.drop(['categories'], axis = 1)


df_travail4.head()


df_travail4.describe()


for col in df_travail4.select_dtypes('float'):
    plt.figure()
    sns.displot(df_travail4[col])

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>


df_test = df_travail4.copy()


df_travail4 = df_travail4.sort_values('nutrition_grade_fr')


plt.figure()
df_travail4['nutrition_grade_fr'].hist()

<AxesSubplot:>


df_travail4.groupby(by  = 'nutrition_grade_fr').mean()


sns.boxplot(x='nutrition_grade_fr', y='nutrition-score-fr_100g', data = df_travail4)

<AxesSubplot:xlabel='nutrition_grade_fr', ylabel='nutrition-score-fr_100g'>


import scipy.stats as stat
data_a_energy = df_travail4['energy_100g'][df_travail4['nutrition_grade_fr']=='a']
data_b_energy = df_travail4['energy_100g'][df_travail4['nutrition_grade_fr']=='b']
#data_b_energy = df_travail4[df_travail4['nutrition_grade_fr']=='b']['energy_100g']
data_c_energy = df_travail4['energy_100g'][df_travail4['nutrition_grade_fr']=='c']
#data_d_energy = df_travail4[df_travail4['nutrition_grade_fr']=='d']['energy_100g']
#data_e_energy = df_travail4[df_travail4['nutrition_grade_fr']=='e']['energy_100g']


data_a_ns = df_travail4['nutrition-score-fr_100g'][df_travail4['nutrition_grade_fr']=='a']
data_b_ns = df_travail4['nutrition-score-fr_100g'][df_travail4['nutrition_grade_fr']=='b']


print (stat.f_oneway( data_a_ns, data_b_ns))

F_onewayResult(statistic=14831.866715284736, pvalue=0.0)


print (stat.f_oneway( data_a_energy, data_b_energy))

F_onewayResult(statistic=40.05834324444558, pvalue=2.665238264954294e-10)


sample_a = data_a_energy.sample( n = 150)
sample_b = data_b_energy.sample( n = 150)
sample_c = data_c_energy.sample( n = 150)


print (stat.f_oneway(sample_a, sample_c))

F_onewayResult(statistic=20.112183435143052, pvalue=1.0434876011686271e-05)


import scipy.stats as stat
data_a_s_fat = df_travail4[df_travail4['nutrition_grade_fr']=='a']['saturated-fat_100g']
data_b_s_fat = df_travail4[df_travail4['nutrition_grade_fr']=='b']['saturated-fat_100g']
data_c_s_fat = df_travail4[df_travail4['nutrition_grade_fr']=='c']['saturated-fat_100g']
data_d_s_fat = df_travail4[df_travail4['nutrition_grade_fr']=='d']['saturated-fat_100g']
data_e_s_fat = df_travail4[df_travail4['nutrition_grade_fr']=='e']['saturated-fat_100g']


print (stat.f_oneway( data_a_s_fat, data_b_s_fat))

F_onewayResult(statistic=421.86530686595495, pvalue=2.6523267247577134e-90)


import scipy.stats as stat
data_a_ns = df_travail4[df_travail4['nutrition_grade_fr']=='a']['nutrition-score-fr_100g']
data_b_ns = df_travail4[df_travail4['nutrition_grade_fr']=='b']['nutrition-score-fr_100g']
data_c_ns = df_travail4[df_travail4['nutrition_grade_fr']=='c']['nutrition-score-fr_100g']
data_d_ns = df_travail4[df_travail4['nutrition_grade_fr']=='d']['nutrition-score-fr_100g']
data_e_ns = df_travail4[df_travail4['nutrition_grade_fr']=='e']['nutrition-score-fr_100g']


print (stat.f_oneway(data_a_ns , data_b_ns, data_c_ns, data_d_ns, data_e_ns ))

F_onewayResult(statistic=44921.58998225341, pvalue=0.0)


sns.pairplot(df_travail4, hue= 'nutrition_grade_fr')

<seaborn.axisgrid.PairGrid at 0x1d28b1d7a60>


sns.heatmap(df_travail4.corr())

<AxesSubplot:>


df_travail4.shape

(13160, 10)


df_travail4.head()


df_travail4.dtypes

product_name                object
nutrition_grade_fr          object
energy_100g                float64
proteins_100g              float64
sugars_100g                float64
fat_100g                   float64
saturated-fat_100g         float64
fiber_100g                 float64
sodium_100g                float64
nutrition-score-fr_100g    float64
dtype: object


df_travail5 = df_travail4.copy()


df_travail5.head()


df_travail5['nutrition_grade_fr'].replace(['a', 'b', 'c', 'd', 'e'], [1,2,3,4,5], inplace = True)


df_travail5 = df_travail5.set_index('product_name')


n=df_travail5.shape[0]
p=df_travail5.shape[1]
n,p

(13160, 9)


# Je vérifie que le "nutrigrade" est bien un entier à présent
df_travail5.dtypes

nutrition_grade_fr           int64
energy_100g                float64
proteins_100g              float64
sugars_100g                float64
fat_100g                   float64
saturated-fat_100g         float64
fiber_100g                 float64
sodium_100g                float64
nutrition-score-fr_100g    float64
dtype: object


from sklearn.preprocessing import StandardScaler
#instanciation
sc = StandardScaler()
#transformation – centrage-réduction
Z = sc.fit_transform(df_travail5.values)
print(Z)

[[-1.44297814  0.82868857  1.44674928 ...  1.78098481 -0.1881548
  -1.39878909]
 [-1.44297814 -1.20083551 -0.90222688 ... -0.44843372 -0.85015961
  -1.13788903]
 [-1.44297814 -1.2416072  -0.90222688 ... -0.39535233 -0.85015961
  -1.13788903]
 ...
 [ 1.70112424  1.73321605 -0.0501473  ... -0.28918954  0.25869844
   1.99291165]
 [ 1.70112424  0.67315213 -0.41861414 ... -1.29773602 -0.81705937
   1.60156157]
 [ 1.70112424 -1.36996251 -1.43189798 ... -1.08541045 -1.29254432
   0.68841137]]


Z.shape

(13160, 9)


print(np.mean(Z,axis=0))

[ 0.00000000e+00 -2.76442159e-16  1.07985218e-16  1.38221079e-16
  1.38221079e-16 -1.38221079e-16  2.28928663e-16  3.45552698e-17
 -6.91105396e-17]


print(np.std(Z,axis=0,ddof=0))

[1. 1. 1. 1. 1. 1. 1. 1. 1.]


#classe pour l'ACP
from sklearn.decomposition import PCA
#instanciation
acp = PCA(svd_solver='full')


#calculs
coord = acp.fit_transform(Z)
#nombre de composantes calculées
print(acp.n_components_)

9


print(coord)

[[-1.37225705  1.53378916  2.57862576 ... -0.51076354 -0.34739898
  -0.11863222]
 [-2.67273831 -0.9314617   0.12220334 ...  0.03762609 -0.0820047
   0.20474736]
 [-2.67794763 -0.98376747  0.19952655 ...  0.12250838 -0.04012203
   0.21044824]
 ...
 [ 4.33727262 -0.03235581 -1.12627646 ... -0.17565844 -0.59077096
  -0.07156918]
 [ 3.46673932 -1.36740325 -1.79515761 ...  0.03868344 -0.20588784
  -0.23790963]
 [-0.6710282  -2.24745136 -1.55695092 ... -1.18189874  1.28585257
   0.11021871]]


len(coord[0])

9


coord.shape

(13160, 9)


df_projected = pd.DataFrame(coord, index= df_travail5.index, columns=['F'+str(i+1) for i in range(9)])


df_projected.head()


f1 =acp.components_[0]
print(f1)
f2 =acp.components_[1]
print(f2)

[0.41895253 0.40509753 0.0708155  0.32246872 0.40479156 0.40282147
 0.15452798 0.03024565 0.44772628]
[-0.05924613  0.14049778  0.5831792  -0.40406285  0.14377817 -0.06839321
  0.18955594  0.64000209 -0.05001582]


def display_scree_plot(pca):
    scree = pca.explained_variance_ratio_*100
    plt.bar(np.arange(len(scree))+1, scree)
    plt.plot(np.arange(len(scree))+1, scree.cumsum(),c="red",marker='o')
    plt.xlabel("rang de l'axe d'inertie")
    plt.ylabel("pourcentage d'inertie")
    plt.title("Eboulis des valeurs propres")
    plt.show(block=False)


from sklearn import decomposition
from sklearn import preprocessing
display_scree_plot(acp)


f1 =acp.components_[0]
print(f1)
f2 =acp.components_[1]
print(f2)

[0.41895253 0.40509753 0.0708155  0.32246872 0.40479156 0.40282147
 0.15452798 0.03024565 0.44772628]
[-0.05924613  0.14049778  0.5831792  -0.40406285  0.14377817 -0.06839321
  0.18955594  0.64000209 -0.05001582]


import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram

def display_circles(pcs, n_comp, pca, axis_ranks, labels=None, label_rotation=0, lims=None):
    for d1, d2 in axis_ranks: # On affiche les 3 premiers plans factoriels, donc les 6 premières composantes
        if d2 < n_comp:

            # initialisation de la figure
            fig, ax = plt.subplots(figsize=(16,16))

            # détermination des limites du graphique
            if lims is not None :
                xmin, xmax, ymin, ymax = lims
            elif pcs.shape[1] < 30 :
                xmin, xmax, ymin, ymax = -1, 1, -1, 1
            else :
                xmin, xmax, ymin, ymax = min(pcs[d1,:]), max(pcs[d1,:]), min(pcs[d2,:]), max(pcs[d2,:])

            # affichage des flèches
            # s'il y a plus de 30 flèches, on n'affiche pas le triangle à leur extrémité
            if pcs.shape[1] < 30 :
                plt.quiver(np.zeros(pcs.shape[1]), np.zeros(pcs.shape[1]),
                   pcs[d1,:], pcs[d2,:], 
                   angles='xy', scale_units='xy', scale=1, color="grey")
                # (voir la doc : https://matplotlib.org/api/_as_gen/matplotlib.pyplot.quiver.html)
            else:
                lines = [[[0,0],[x,y]] for x,y in pcs[[d1,d2]].T]
                ax.add_collection(LineCollection(lines, axes=ax, alpha=.1, color='black'))
            
            # affichage des noms des variables  
            if labels is not None:  
                for i,(x, y) in enumerate(pcs[[d1,d2]].T):
                    if x >= xmin and x <= xmax and y >= ymin and y <= ymax :
                        plt.text(x, y, labels[i], fontsize='14', ha='center', va='center', rotation=label_rotation, color="blue", alpha=0.5)
            
            # affichage du cercle
            circle = plt.Circle((0,0), 1, facecolor='none', edgecolor='b')
            plt.gca().add_artist(circle)

            # définition des limites du graphique
            plt.xlim(xmin, xmax)
            plt.ylim(ymin, ymax)

        
            # affichage des lignes horizontales et verticales
            plt.plot([-1, 1], [0, 0], color='grey', ls='--')
            plt.plot([0, 0], [-1, 1], color='grey', ls='--')

            # nom des axes, avec le pourcentage d'inertie expliqué
            plt.xlabel('F{} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('F{} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Cercle des corrélations (F{} et F{})".format(d1+1, d2+1))
            plt.show(block=False)
        
def display_factorial_planes(X_projected, n_comp, pca, axis_ranks, labels=None, alpha=1, illustrative_var=None, illustrative_legend=None):
    for d1,d2 in axis_ranks:
        if d2 < n_comp:
 
            # initialisation de la figure       
            fig = plt.figure(figsize=(10,6))
        
            # affichage des points
            if illustrative_var is None:
                plt.scatter(X_projected[:, d1], X_projected[:, d2], alpha=alpha)
            else:
                illustrative_var = np.array(illustrative_var)
                for value in np.unique(illustrative_var):
                    selected = np.where(illustrative_var == value)
                    plt.scatter(X_projected[selected, d1], X_projected[selected, d2], alpha=alpha, label=value)
                plt.legend()

            # affichage des labels des points
            if labels is not None:
                for i,(x,y) in enumerate(X_projected[:,[d1,d2]]):
                    plt.text(x, y, labels[i],
                              fontsize='14', ha='center',va='center') 
                
            # détermination des limites du graphique
            boundary = np.max(np.abs(X_projected[:, [d1,d2]])) * 1.1
            plt.xlim([-boundary,boundary])
            plt.ylim([-boundary,boundary])
        
            # affichage des lignes horizontales et verticales
            plt.plot([-100, 100], [0, 0], color='grey', ls='--')
            plt.plot([0, 0], [-100, 100], color='grey', ls='--')

            # nom des axes, avec le pourcentage d'inertie expliqué
            plt.xlabel('F{} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('F{} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Projection des aliments (coloration : "+illustrative_legend+") (sur F{} et F{})".format(d1+1, d2+1))
            plt.show(block=False)

def display_scree_plot(pca):
    scree = pca.explained_variance_ratio_*100
    plt.bar(np.arange(len(scree))+1, scree)
    plt.plot(np.arange(len(scree))+1, scree.cumsum(),c="red",marker='o')
    plt.xlabel("rang de l'axe d'inertie")
    plt.ylabel("pourcentage d'inertie")
    plt.title("Eboulis des valeurs propres")
    plt.show(block=False)

def plot_dendrogram(Z, names):
    plt.figure(figsize=(10,25))
    plt.title('Hierarchical Clustering Dendrogram')
    plt.xlabel('distance')
    dendrogram(
        Z,
        labels = names,
        orientation = "left",
    )
    plt.show()


from sklearn import decomposition
from sklearn import preprocessing

# Import `PCA` from `sklearn.decomposition`
from sklearn.decomposition import PCA

# Build the model
pca = PCA(n_components=2)

# choix du nombre de composantes à calculer
n_comp = 6

# import de l'échantillon
data = df_travail5

# selection des colonnes à prendre en compte dans l'ACP
data_pca = df_travail5[['nutrition_grade_fr', 'energy_100g', 'proteins_100g', 'sugars_100g', 'fat_100g', 'saturated-fat_100g', 'fiber_100g', 'sodium_100g', 'nutrition-score-fr_100g']]

# préparation des données pour l'ACP
#data_pca = data_pca.fillna(data_pca.mean()) # Il est fréquent de remplacer les valeurs inconnues par la moyenne de la variable
data_pca = data_pca.dropna()

X = data_pca.values
#names = data["idCours"] # ou data.index pour avoir les intitulés

#features = data.columns
features = data_pca.columns

# Centrage et Réduction
std_scale = preprocessing.StandardScaler().fit(X)
X_scaled = std_scale.transform(X)

# Calcul des composantes principales
pca = decomposition.PCA(n_components=n_comp)
pca.fit(X_scaled)

# Eboulis des valeurs propres
display_scree_plot(pca)


# Cercle des corrélations
pcs = pca.components_
#plt.figure(figsize=(16,10))
plt.rcParams["figure.figsize"] = [16,9]
display_circles(pcs, n_comp, pca, [(0,1),(2,3)], labels = np.array(features))


import scipy.stats as stats
X1 = df_travail5['nutrition_grade_fr']
X2 = df_travail5['fat_100g']
y = stats.ttest_ind(X1,X2) ; print(y)
X1.shape

Ttest_indResult(statistic=-94.21027545058314, pvalue=0.0)

(13160,)


# Projection des individus
X_projected = pca.transform(X_scaled)
display_factorial_planes(X_projected, n_comp, pca, [(0,1),(2,3)], illustrative_var=data_pca[['nutrition_grade_fr']].values[:,0], illustrative_legend='nutrition_grade_fr')


plt.show()


X_projected

array([[-1.37225705,  1.53378916,  2.57862576,  0.63230832, -0.16055507,
         0.042369  ],
       [-2.67273831, -0.9314617 ,  0.12220334, -0.1503169 , -0.65920177,
         0.04649386],
       [-2.67794763, -0.98376747,  0.19952655, -0.15236321, -0.52891433,
         0.03895406],
       ...,
       [ 4.33727262, -0.03235581, -1.12627646, -0.02276101, -0.9887938 ,
         0.10761239],
       [ 3.46673932, -1.36740325, -1.79515761,  0.61401129, -1.25853267,
         0.20728712],
       [-0.6710282 , -2.24745136, -1.55695092, -0.23580044,  0.97128145,
         0.33836995]])


sns.heatmap(df_travail5.corr())

<AxesSubplot:>


from sklearn.cluster import KMeans
num_components = 7
principalDf = pd.DataFrame(X_projected, columns = range(1,num_components))
# Run a number of tests, for 1, 2, ... num_clusters
num_clusters = 9
kmeans_tests = [KMeans(n_clusters=i, init='random', n_init=10) for i in range(1, num_clusters)]
score = [kmeans_tests[i].fit(principalDf).score(principalDf) for i in range(len(kmeans_tests))]


from sklearn.neighbors import KNeighborsClassifier


model = KNeighborsClassifier()


y= df_travail5['nutrition_grade_fr']
X = df_travail5.drop(['nutrition_grade_fr'], axis = 1)


model.fit(X,y)

KNeighborsClassifier()


model.predict(X)

array([1, 1, 1, ..., 5, 5, 5], dtype=int64)


model.score(X,y)

0.86580547112462


import random
n = random.randint(0, 13159)
features = X.iloc[n]
features, n

(energy_100g                155.0
 proteins_100g                0.9
 sugars_100g                  1.4
 fat_100g                     1.2
 saturated-fat_100g           0.4
 fiber_100g                   1.7
 sodium_100g                  0.7
 nutrition-score-fr_100g      4.0
 Name: Pur Soup' - Mouliné de 7 légumes variés, dtype: float64,
 7859)


def nutriscore (model, list):
    x = np.array([list]).reshape(1, len(list))
    print(model.predict(x))
    print(model.predict_proba(x))


nutriscore(model, features)

[3]
[[0.  0.2 0.8 0.  0. ]]


from sklearn.cluster import KMeans


model_c = KMeans (n_clusters=5 )
model_c.fit(X_projected)

KMeans(n_clusters=5)


model_c.predict(X_projected)

array([3, 1, 1, ..., 0, 0, 1])


palette = ['blue', 'orange', 'green', 'red', 'purple' ]


sns.scatterplot(X_projected[:,0], X_projected[:,1], hue=model_c.predict(X_projected), palette = palette)

C:\Users\psiam\anaconda3\envs\OpenFood2\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:>


sns.scatterplot(X_projected[:,0], X_projected[:,2], hue=model_c.predict(X_projected), palette = palette)

C:\Users\psiam\anaconda3\envs\OpenFood2\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:>


sns.scatterplot(X_projected[:,1], X_projected[:,2], hue=model_c.predict(X_projected), palette = palette)

C:\Users\psiam\anaconda3\envs\OpenFood2\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:>


X_projected

array([[-1.37225705,  1.53378916,  2.57862576,  0.63230832, -0.16055507,
         0.042369  ],
       [-2.67273831, -0.9314617 ,  0.12220334, -0.1503169 , -0.65920177,
         0.04649386],
       [-2.67794763, -0.98376747,  0.19952655, -0.15236321, -0.52891433,
         0.03895406],
       ...,
       [ 4.33727262, -0.03235581, -1.12627646, -0.02276101, -0.9887938 ,
         0.10761239],
       [ 3.46673932, -1.36740325, -1.79515761,  0.61401129, -1.25853267,
         0.20728712],
       [-0.6710282 , -2.24745136, -1.55695092, -0.23580044,  0.97128145,
         0.33836995]])


fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

x = X_projected[:,0]
y = X_projected[:,1]
z = X_projected[:,2]

ax.scatter(x, y, z,c=model_c.predict(X_projected))

plt.show()


from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets


import ipyvolume as ipv


import ipyvolume.pylab as p3
import numpy as np
import matplotlib.cm
#points
x = X_projected[:,0]
y = X_projected[:,1]
z = X_projected[:,2]

#colors
colormap = matplotlib.cm.viridis
c = colormap(model_c.predict(X_projected)/4, len(x))
p3.clear()
p3.scatter(x, y, z, color=c, size=3)
p3.show()

	product_name	categories	nutrition_grade_fr	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	fruits-vegetables-nuts_100g	nutrition-score-fr_100g
0	Farine de blé noir	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
46	Filet de bœuf	Filet de bœuf	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
48	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
136	Twix x2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
182	lentilles vertes	Aliments et boissons à base de végétaux,Alimen...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
183	NaN	Root bier	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
184	Biscuits sablés fourrage au cacao	Snacks sucrés,Biscuits et gâteaux,Biscuits,Sablés	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
187	NaN	Bonbons	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
188	Thé de Noël aromatisé orange-cannelle	Aliments et boissons à base de végétaux,Boisso...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
189	Sirops pour ganache macarons	Sirops,Sirops pour ganache	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
190	Mini Confettis	NaN	d	1753.0	0.6	87.7	NaN	0.8	0.9	0.003937	NaN	14.0
191	Praliné Amande Et Noisette	NaN	d	2406.0	9.5	50.3	NaN	2.9	3.9	0.001181	NaN	14.0
194	Côte du Rhône village	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
224	Bonbon Vichy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
225	M&m's peanut butter	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
226	Pepsi, Nouveau goût !	Sodas au cola	e	177.0	0.0	10.4	0.0	0.0	0.0	0.010000	NaN	13.0
227	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
232	BoFrost Nudelpfanne	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
234	Tarte Poireaux Et Lardons	NaN	d	1079.0	7.5	1.0	NaN	11.0	1.4	0.314961	NaN	15.0
240	7Up	Sodas au citron,Boissons sucrées	e	177.0	0.0	10.4	0.0	0.0	NaN	0.039370	NaN	13.0
242	Madeleines nature	Snacks sucrés,Desserts,Biscuits et gâteaux,Gât...	d	1900.0	6.0	26.0	23.0	2.5	1.5	0.259843	NaN	12.0
271	Terrine de campagne	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
279	Cakes Raisins	Cakes aux raisins,Cakes,Gâteaux	d	1768.0	5.8	28.0	19.0	2.5	1.8	0.255906	NaN	13.0
280	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
283	Cakes aux Fruits	Snacks sucrés,Desserts,Biscuits et gâteaux,Gât...	d	1670.0	4.9	34.0	15.0	2.0	1.5	0.236220	37.4	12.0

	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
count	5.811400e+04	58114.000000	58114.000000	41139.000000	58114.000000	42596.000000	58114.000000	58185.000000
mean	1.177851e+03	7.777226	13.202414	13.248042	5.398351	2.509733	0.428347	8.597353
std	1.352981e+04	7.880345	18.959685	16.751020	8.549225	4.621001	1.514282	9.019050
min	0.000000e+00	0.000000	-0.100000	0.000000	0.000000	0.000000	0.000000	-15.000000
25%	4.340000e+02	1.900000	1.000000	1.400000	0.300000	0.000000	0.031496	1.000000
50%	1.033000e+03	6.000000	4.000000	6.900000	2.000000	1.300000	0.224409	8.000000
75%	1.648000e+03	11.000000	17.000000	21.000000	7.300000	3.200000	0.492126	15.000000
max	3.251373e+06	100.000000	104.000000	380.000000	210.000000	178.000000	83.000000	40.000000

	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
count	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000
mean	1094.222356	6.717755	11.654151	10.695400	3.891062	2.544804	0.312476	6.722796
std	662.249382	4.342482	13.531523	9.485468	4.473016	1.883971	0.237893	7.666062
min	126.000000	0.004000	0.010000	0.001000	0.001000	0.001000	0.000002	-4.000000
25%	485.000000	3.500000	1.800000	2.900000	0.700000	1.100000	0.100000	0.000000
50%	990.500000	6.300000	4.600000	7.500000	2.100000	2.100000	0.295276	5.000000
75%	1686.000000	8.700000	20.000000	16.712500	5.400000	3.500000	0.452756	13.000000
max	2367.000000	22.900000	55.900000	42.000000	19.800000	8.900000	1.181000	23.000000

	product_name	nutrition_grade_fr	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
242	Madeleines nature	d	1900.0	6.0	26.0	23.0	2.5	1.5	0.259843	12.0
279	Cakes Raisins	d	1768.0	5.8	28.0	19.0	2.5	1.8	0.255906	13.0
283	Cakes aux Fruits	d	1670.0	4.9	34.0	15.0	2.0	1.5	0.236220	12.0
299	Financiers aux Amandes	e	1868.0	8.0	39.0	23.0	10.0	1.7	0.216535	22.0
301	Fondants Citron	e	1896.0	8.1	37.0	24.0	10.0	2.5	0.307087	22.0

	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
count	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000	13160.000000
mean	1094.222356	6.717755	11.654151	10.695400	3.891062	2.544804	0.312476	6.722796
std	662.249382	4.342482	13.531523	9.485468	4.473016	1.883971	0.237893	7.666062
min	126.000000	0.004000	0.010000	0.001000	0.001000	0.001000	0.000002	-4.000000
25%	485.000000	3.500000	1.800000	2.900000	0.700000	1.100000	0.100000	0.000000
50%	990.500000	6.300000	4.600000	7.500000	2.100000	2.100000	0.295276	5.000000
75%	1686.000000	8.700000	20.000000	16.712500	5.400000	3.500000	0.452756	13.000000
max	2367.000000	22.900000	55.900000	42.000000	19.800000	8.900000	1.181000	23.000000

Analyse du Data Set OpenFoodFatcs¶

Ressources utilisées:¶

1) Exploratory Data Analysis¶

Checklist de base¶

Analyse de forme¶

Analyse de fond¶

Analyse de la forme des données¶

Visualisation des valeurs manquantes¶

Remarque:¶

Choix (élimination) des variables facultatives¶

Elimination des outliers¶

histogramme des valeurs continues¶

observation de la variable qualitative (nutrigrade)¶

Analyse bivariée (ANOVA)¶

Anlyse Multivariée , ACP¶

1ère conclusion:¶

? WTF : la p-value indiquant 0, on doit rejeter l'hypothèse nulle et donc: pas de corrélation entre nutrigrade et fat???¶

Utilisation de l'algorithme K-means pour regrouper les 5 clusters comme autant de nuntrigrades¶

Commentaire:¶

Représentations des individus en 3D delon F1, F2 et F3¶

1) de façon projective¶

2) avec une carte interactive¶

	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
nutrition_grade_fr
a	723.483294	6.959808	3.697393	3.928148	0.809167	2.888109	0.221938	-2.290888
b	646.055853	5.763072	4.842309	5.208331	1.334411	1.947870	0.296131	0.949911
c	1037.084197	6.956178	9.770187	9.512599	2.612184	2.579039	0.362596	6.308207
d	1545.683875	7.231734	18.741354	17.261585	6.542631	2.680629	0.370348	14.011518
e	1852.416537	6.512122	29.414217	23.041306	12.142974	2.735177	0.266437	20.342796

	product_name	nutrition_grade_fr	energy_100g	proteins_100g	sugars_100g	fat_100g	saturated-fat_100g	fiber_100g	sodium_100g	nutrition-score-fr_100g
320751	Tartines craquantes bio au sarrasin	a	1643.0	13.0	2.6	2.8	0.6	5.9	0.267717	-4.0
212627	Petit Repas Pommes de terre au jambon	a	299.0	2.8	0.6	2.2	0.7	1.7	0.110236	-2.0
212628	Petits repas Légumes & Volaille à la basquaise...	a	272.0	2.8	2.6	2.0	0.3	1.8	0.110236	-2.0
212642	Paëlla Poulet, Chorizo, Fruits de mer, à l'hui...	a	623.0	9.0	0.8	5.9	1.7	3.0	0.311024	-4.0
270562	Top Porridge	a	1527.0	18.4	14.3	6.3	1.1	6.6	0.145669	-1.0

	F1	F2	F3	F4	F5	F6	F7	F8	F9
product_name
Tartines craquantes bio au sarrasin	-1.372257	1.533789	2.578626	0.632308	-0.160555	0.042369	-0.510764	-0.347399	-0.118632
Petit Repas Pommes de terre au jambon	-2.672738	-0.931462	0.122203	-0.150317	-0.659202	0.046494	0.037626	-0.082005	0.204747
Petits repas Légumes & Volaille à la basquaise avec morceaux	-2.677948	-0.983767	0.199527	-0.152363	-0.528914	0.038954	0.122508	-0.040122	0.210448
Paëlla Poulet, Chorizo, Fruits de mer, à l'huile d'olive vierge extra 1,4%	-2.105350	0.688821	0.712317	0.324753	-0.574170	0.163492	0.346053	-0.103098	-0.110533
Top Porridge	-0.664804	1.652933	3.164062	1.789924	0.077446	0.212351	0.079887	0.180135	0.224506